Multiple testing with minimal assumptions.
Resampling-based multiple testing methods that control the Familywise Error Rate in the strong sense are presented. It is shown that no assumptions whatsoever on the data-generating process are required to obtain a reasonably powerful and flexible class of multiple testing procedures. Improvements are obtained with mild assumptions. The methods are applicable to gene expression data in particular, but more generally to any multivariate, multiple group data that may be character or numeric. The role of the disputed "subset pivotality" condition is clarified.
Westfall
Peter H
PH
Texas Tech University, Lubbock, TX, USA. peter.westfall@ttu.edu
Troendle
James F
JF
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis